发现与卵巢癌细胞死亡和细胞外基质模块相关的分子生物标志物。
Discover the molecular biomarker associated with cell death and extracellular matrix module in ovarian cancer.
作者信息
Liu Qiang, Guo Jianxin, Cui Jinghong, Wang Jing, Yi Ping
机构信息
Department of Obstetrics and Gynecology, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing 40042, China.
出版信息
Biomed Res Int. 2015;2015:735689. doi: 10.1155/2015/735689. Epub 2015 Mar 16.
High throughput technologies have provided many new research methods for ovarian cancer investigation. In tradition, in order to find the underlying functional mechanisms of the survival-associated genes, gene sets enrichment analysis (GSEA) is always regarded as the important choice. However, GSEA produces too many candidate genes and cannot discover the signaling transduction cascades. In this work, we have used a network-based strategy to optimize the discovery of biomarkers using multifactorial data, including patient expression, clinical survival, and protein-protein interaction (PPI) data. The biomarkers discovered by this strategy belong to the network-based biomarker, which is apt to reveal the underlying functional mechanisms of the biomarker. In this work, over 400 expression arrays in ovarian cancer have been analyzed: the results showed that cell death and extracellular module are the main themes related to ovarian cancer progression.
高通量技术为卵巢癌研究提供了许多新的研究方法。传统上，为了找到生存相关基因的潜在功能机制，基因集富集分析（GSEA）一直被视为重要选择。然而，GSEA产生了太多候选基因，无法发现信号转导级联反应。在这项工作中，我们使用了一种基于网络的策略，利用多因素数据（包括患者表达、临床生存和蛋白质-蛋白质相互作用（PPI）数据）来优化生物标志物的发现。通过该策略发现的生物标志物属于基于网络的生物标志物，它易于揭示生物标志物的潜在功能机制。在这项工作中，已经分析了400多个卵巢癌表达阵列：结果表明，细胞死亡和细胞外模块是与卵巢癌进展相关的主要主题。
Zotero 插件